Data Scientist

Danta Technologies

$135K — $160K *
Information Technology
8 - 10 years of experience
Job Overview by Ladders

Qualifications

  • PhD or Master's degree in Computer Science, Machine Learning, or a related field.
  • 8+ years of applied AI/ML experience with proven production-grade model delivery.
  • Deep expertise in training and fine-tuning various Large Language Models (LLMs).
  • Strong background in graph-based retrieval systems and knowledge graphs.
  • Familiarity with semantic search technologies and vector databases.
  • Experience with distributed training frameworks and high-performance networking.
  • Knowledge of optimization algorithms and reinforcement learning techniques.

Responsibilities

  • Lead comprehensive training and fine-tuning processes for LLMs in both open-source and closed-source environments.
  • Design and implement innovative GraphRAG pipelines to enhance data retrieval.
  • Create and optimize semantic and dense vector embeddings for effective document processing.
  • Develop advanced systems for semantic retrieval, including document segmentation strategies.
  • Build and scale efficient distributed training environments using NCCL and InfiniBand.
  • Apply reinforcement learning to ensure model alignment with human preferences.
  • Collaborate with multi-disciplinary teams to convert business needs into AI-enhanced solutions.

Benefits

  • Competitive pay aligned with industry standards.
  • Options for healthcare insurance including dental, medical, and vision.
  • Paid sick leave as per state law.
  • Major holidays off.
Full Job Description
Job Details:
Work Experience:

Lead end-to-end training and fine-tuning of Large Language Models (LLMs), including both open-source (e.g., Qwen, LLaMA, Mistral) and closed-source (e.g., OpenAI, Gemini, Anthropic) ecosystems.
Architect and implement GraphRAG pipelines, including knowledge graph representation and retrieval for enhanced contextual grounding.
Design, train, and optimize semantic and dense vector embeddings for document understanding, search, and retrieval.
Develop semantic retrieval systems with advanced document segmentation and indexing strategies.
Build and scale distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training.
Apply reinforcement learning techniques (e.g., RLHF, RLAIF) to align model behavior with human preferences and domain-specific goals.
Collaborate with cross-functional teams to translate business needs into AI-driven solutions and deploy them in production environments.

Qualifications
PhD or Master's degree in Computer Science, Machine Learning, or related field.
8+ years of experience in applied AI/ML, with a strong track record of delivering production-grade models.
Deep expertise in:
LLM training and fine-tuning (e.g., GPT, LLaMA, Mistral, Qwen)
Graph-based retrieval systems (GraphRAG, knowledge graphs)
Embedding models (e.g., BGE, E5, SimCSE)
Semantic search and vector databases (e.g., FAISS, Weaviate, Milvus)
Document segmentation and preprocessing (OCR, layout parsing)
Distributed training frameworks (NCCL, Horovod, DeepSpeed)
High-performance networking (InfiniBand, RDMA)
Model fusion and ensemble techniques (stacking, boosting, gating)
Optimization algorithms (Bayesian, Particle Swarm, Genetic Algorithms)
Symbolic AI and rule-based systems
Meta-learning and Mixture of Experts architectures
Reinforcement learning (e.g., RLHF, PPO, DPO)

Bonus Skills
Experience with healthcare data and medical coding systems (e.g., CPT, CM, PCS).
Familiarity with regulatory and compliance frameworks in AI deployment.
Contributions to open-source AI projects or published research. And/Or ability to take research papers to poc - production.

Benefits: Danta offers a compensation package to all W2 employees that are competitive in the industry. It consists of competitive pay, the option to elect healthcare insurance (Dental, Medical, Vision), Major holidays and Paid sick leave as per state law.

The rate/ Salary range is dependent on numerous factors including Qualification, Experience and Location.

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